Learning from the "Dizeez" game

We recently released a game called Dizeez that tests your knowledge of gene-disease links. (Haven’t seen it yet? Play here.) Now that it’s been live for a couple of weeks, we’ve had a chance to look at the game logs and make a few observations:

Dizeez was played to completion over 300 times by over 100 unique players

So why have we created this game? It follows in a line of recent ideas in our lab about how to productively harness community intelligence. The logic goes something like this… Generally, the gene-disease links in structured databases will be reasonably correct (though likely not at all complete). When we analyze the game logs in aggregate, we expect that players’ answers will generally reinforce what’s already known. But given enough game player data, also expect that we’ll see multiple instances of gene-disease links that aren’t reflected in current annotation databases. And these are candidate novel annotations.

Why are new methods for generating gene-disease (and gene-function) annotations important? Because biology is generating data in droves these days, and many analysis methods (e.g., GSEA and similar) make the assumption that gene annotations are generally correct and complete. If you have ever looked at the GO annotations for a gene you know really well, I think you’ll find that assumption to be generally false. Filling this gap between the knowledge represented in the biomedical literature and the knowledge in gene annotation databases is the motivating factor behind our interest in games, and more broadly, in community intelligence.

We haven’t have quite enough game play yet to identify any strong candidate annotations. (My guess is that we’ll need at least ten times more games played.) But the activity and enthusiasm around this game design tells us we’re on the right path, especially given the limited coding (a few days) and publicity (a few blog posts and tweets) we’ve invested so far.